Add a new reranking based on mosec. (#210)

Signed-off-by: Jincheng Miao <[email protected]>
opea-project · Jun 24, 2024 · a58ca4a · a58ca4a
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diff --git a/comps/reranks/langchain-mosec/README.md b/comps/reranks/langchain-mosec/README.md
@@ -0,0 +1,33 @@
+# build reranking Mosec endpoint docker image
+
+```
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t reranking-langchain-mosec:latest -f comps/reranks/langchain-mosec/mosec-docker/Dockerfile .
+```
+
+# build reranking microservice docker image
+
+```
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/reranking-langchain-mosec:latest -f comps/reranks/langchain-mosec/docker/Dockerfile .
+```
+
+# launch Mosec endpoint docker container
+
+```
+docker run -d --name="reranking-langchain-mosec-endpoint" -p 6001:8000  reranking-langchain-mosec:latest
+```
+
+# launch embedding microservice docker container
+
+```
+export MOSEC_RERANKING_ENDPOINT=http://127.0.0.1:6001
+docker run -d --name="reranking-langchain-mosec-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 6000:8000 --ipc=host -e MOSEC_RERANKING_ENDPOINT=$MOSEC_RERANKING_ENDPOINT opea/reranking-langchain-mosec:latest
+```
+
+# run client test
+
+```
+curl http://localhost:6000/v1/reranking \
+   -X POST \
+   -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
+   -H 'Content-Type: application/json'
+```
diff --git a/comps/reranks/langchain-mosec/__init__.py b/comps/reranks/langchain-mosec/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
diff --git a/comps/reranks/langchain-mosec/docker/Dockerfile b/comps/reranks/langchain-mosec/docker/Dockerfile
@@ -0,0 +1,28 @@
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM langchain/langchain:latest
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+USER user
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /home/user/comps/reranks/langchain-mosec/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+WORKDIR /home/user/comps/reranks/langchain-mosec
+
+ENTRYPOINT ["python", "reranking_mosec_xeon.py"]
+
diff --git a/comps/reranks/langchain-mosec/docker/docker_compose_embedding.yaml b/comps/reranks/langchain-mosec/docker/docker_compose_embedding.yaml
@@ -0,0 +1,22 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3.8"
+
+services:
+  reranking:
+    image: opea/reranking-langchain-mosec:latest
+    container_name: reranking-langchain-mosec-server
+    ports:
+      - "6000:8000"
+    ipc: host
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      MOSEC_RERANKING_ENDPOINT: ${MOSEC_RERANKING_ENDPOINT}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge
diff --git a/comps/reranks/langchain-mosec/mosec-docker/Dockerfile b/comps/reranks/langchain-mosec/mosec-docker/Dockerfile
@@ -0,0 +1,23 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+From ubuntu:22.04
+ARG DEBIAN_FRONTEND=noninteractive
+
+ENV GLIBC_TUNABLES glibc.cpu.x86_shstk=permissive
+
+COPY comps /root/comps
+
+RUN apt update && apt install -y python3 python3-pip 
+RUN pip3 install torch==2.2.2 torchvision --trusted-host download.pytorch.org --index-url https://download.pytorch.org/whl/cpu 
+RUN pip3 install intel-extension-for-pytorch==2.2.0
+RUN pip3 install transformers sentence-transformers
+RUN pip3 install llmspec mosec
+
+RUN cd /root/ && export HF_ENDPOINT=https://hf-mirror.com && huggingface-cli download --resume-download BAAI/bge-reranker-large --local-dir /root/bge-reranker-large
+
+ENV EMB_MODEL="/root/bge-reranker-large/"
+
+WORKDIR /root/comps/reranks/langchain-mosec/mosec-docker
+
+CMD ["python3", "server-ipex.py"]
diff --git a/comps/reranks/langchain-mosec/mosec-docker/server-ipex.py b/comps/reranks/langchain-mosec/mosec-docker/server-ipex.py
@@ -0,0 +1,172 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+from os import environ
+from typing import Any, Dict, List, Optional, Union
+
+import intel_extension_for_pytorch as ipex
+import numpy as np
+import torch
+from mosec import Server, Worker
+from mosec.mixin import TypedMsgPackMixin
+from msgspec import Struct
+from sentence_transformers import CrossEncoder
+from torch.utils.data import DataLoader
+from tqdm.autonotebook import tqdm, trange
+
+DEFAULT_MODEL = "/root/bge-reranker-large"
+
+
+class MyCrossEncoder(CrossEncoder):
+    def __init__(
+        self,
+        model_name: str,
+        num_labels: int = None,
+        max_length: int = None,
+        device: str = None,
+        tokenizer_args: Dict = None,
+        automodel_args: Dict = None,
+        trust_remote_code: bool = False,
+        revision: Optional[str] = None,
+        local_files_only: bool = False,
+        default_activation_function=None,
+        classifier_dropout: float = None,
+    ) -> None:
+        super().__init__(
+            model_name,
+            num_labels,
+            max_length,
+            device,
+            tokenizer_args,
+            automodel_args,
+            trust_remote_code,
+            revision,
+            local_files_only,
+            default_activation_function,
+            classifier_dropout,
+        )
+        # jit trace model
+        self.model = ipex.optimize(self.model, dtype=torch.float32)
+        vocab_size = self.model.config.vocab_size
+        batch_size = 16
+        seq_length = 512
+        d = torch.randint(vocab_size, size=[batch_size, seq_length])
+        # t = torch.randint(0, 1, size=[batch_size, seq_length])
+        m = torch.randint(1, 2, size=[batch_size, seq_length])
+        self.model = torch.jit.trace(self.model, [d, m], check_trace=False, strict=False)
+        self.model = torch.jit.freeze(self.model)
+
+    def predict(
+        self,
+        sentences: List[List[str]],
+        batch_size: int = 32,
+        show_progress_bar: bool = None,
+        num_workers: int = 0,
+        activation_fct=None,
+        apply_softmax=False,
+        convert_to_numpy: bool = True,
+        convert_to_tensor: bool = False,
+    ) -> Union[List[float], np.ndarray, torch.Tensor]:
+        input_was_string = False
+        if isinstance(sentences[0], str):  # Cast an individual sentence to a list with length 1
+            sentences = [sentences]
+            input_was_string = True
+
+        inp_dataloader = DataLoader(
+            sentences,
+            batch_size=batch_size,
+            collate_fn=self.smart_batching_collate_text_only,
+            num_workers=num_workers,
+            shuffle=False,
+        )
+
+        iterator = inp_dataloader
+        if show_progress_bar:
+            iterator = tqdm(inp_dataloader, desc="Batches")
+
+        if activation_fct is None:
+            activation_fct = self.default_activation_function
+
+        pred_scores = []
+        self.model.eval()
+        self.model.to(self._target_device)
+        with torch.no_grad():
+            for features in iterator:
+                model_predictions = self.model(**features)
+                logits = activation_fct(model_predictions["logits"])
+
+                if apply_softmax and len(logits[0]) > 1:
+                    logits = torch.nn.functional.softmax(logits, dim=1)
+                pred_scores.extend(logits)
+
+        if self.config.num_labels == 1:
+            pred_scores = [score[0] for score in pred_scores]
+
+        if convert_to_tensor:
+            pred_scores = torch.stack(pred_scores)
+        elif convert_to_numpy:
+            pred_scores = np.asarray([score.cpu().detach().numpy() for score in pred_scores])
+
+        if input_was_string:
+            pred_scores = pred_scores[0]
+
+        return pred_scores
+
+
+class Request(Struct, kw_only=True):
+    query: str
+    docs: List[str]
+
+
+class Response(Struct, kw_only=True):
+    scores: List[float]
+
+
+def float_handler(o):
+    if isinstance(o, float):
+        return format(o, ".10f")
+    raise TypeError("Not serializable")
+
+
+class MosecReranker(Worker):
+    def __init__(self):
+        self.model_name = environ.get("MODEL_NAME", DEFAULT_MODEL)
+        self.model = MyCrossEncoder(self.model_name)
+
+    def serialize(self, data: Response) -> bytes:
+        sorted_list = sorted(data.scores, reverse=True)
+        index_sorted = [data.scores.index(i) for i in sorted_list]
+        res = []
+        for i, s in zip(index_sorted, sorted_list):
+            tmp = {"index": i, "score": "{:.10f}".format(s)}
+            res.append(tmp)
+        return json.dumps(res, default=float_handler).encode("utf-8")
+
+    def forward(self, data: List[Request]) -> List[Response]:
+        sentence_pairs = []
+        inputs_lens = []
+        for d in data:
+            inputs_lens.append(len(d["texts"]))
+            tmp = [[d["query"], doc] for doc in d["texts"]]
+            sentence_pairs.extend(tmp)
+
+        scores = self.model.predict(sentence_pairs)
+        scores = scores.tolist()
+
+        resp = []
+        cur_idx = 0
+        for lens in inputs_lens:
+            resp.append(Response(scores=scores[cur_idx : cur_idx + lens]))
+            cur_idx += lens
+
+        return resp
+
+
+if __name__ == "__main__":
+    MAX_BATCH_SIZE = int(os.environ.get("MAX_BATCH_SIZE", 128))
+    MAX_WAIT_TIME = int(os.environ.get("MAX_WAIT_TIME", 10))
+    server = Server()
+    server.append_worker(MosecReranker, max_batch_size=MAX_BATCH_SIZE, max_wait_time=MAX_WAIT_TIME)
+    server.run()
diff --git a/comps/reranks/langchain-mosec/requirements.txt b/comps/reranks/langchain-mosec/requirements.txt
@@ -0,0 +1,9 @@
+docarray[full]
+fastapi
+langchain
+langchain_community
+openai
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+shortuuid
diff --git a/comps/reranks/langchain-mosec/reranking_mosec_xeon.py b/comps/reranks/langchain-mosec/reranking_mosec_xeon.py
@@ -0,0 +1,76 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright 2024 MOSEC Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import re
+import time
+
+import requests
+from langchain_core.prompts import ChatPromptTemplate
+from langsmith import traceable
+
+from comps import (
+    LLMParamsDoc,
+    SearchedDoc,
+    ServiceType,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict,
+)
+
+
+@register_microservice(
+    name="opea_service@reranking_mosec_xeon",
+    service_type=ServiceType.RERANK,
+    endpoint="/v1/reranking",
+    host="0.0.0.0",
+    port=8000,
+    input_datatype=SearchedDoc,
+    output_datatype=LLMParamsDoc,
+)
+@traceable(run_type="llm")
+@register_statistics(names=["opea_service@reranking_mosec_xeon"])
+def reranking(input: SearchedDoc) -> LLMParamsDoc:
+    print("reranking input: ", input)
+    start = time.time()
+    docs = [doc.text for doc in input.retrieved_docs]
+    url = mosec_reranking_endpoint + "/inference"
+    data = {"query": input.initial_query, "texts": docs}
+    headers = {"Content-Type": "application/json"}
+    response = requests.post(url, data=json.dumps(data), headers=headers)
+    response_data = response.json()
+    best_response = max(response_data, key=lambda response: response["score"])
+    doc = input.retrieved_docs[best_response["index"]]
+    if doc.text and len(re.findall("[\u4E00-\u9FFF]", doc.text)) / len(doc.text) >= 0.3:
+        # chinese context
+        template = "仅基于以下背景回答问题:\n{context}\n问题: {question}"
+    else:
+        template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+        """
+    prompt = ChatPromptTemplate.from_template(template)
+    final_prompt = prompt.format(context=doc.text, question=input.initial_query)
+    statistics_dict["opea_service@reranking_mosec_xeon"].append_latency(time.time() - start, None)
+    return LLMParamsDoc(query=final_prompt.strip())
+
+
+if __name__ == "__main__":
+    mosec_reranking_endpoint = os.getenv("MOSEC_RERANKING_ENDPOINT", "http://localhost:8080")
+    opea_microservices["opea_service@reranking_mosec_xeon"].start()